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Performance Study of Convolutive BSS Algorithms Applied to the Electrocardiogram of Atrial Fibrillation

  • Carlos Vayá
  • José Joaquín Rieta
  • César Sánchez
  • David Moratal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3889)

Abstract

Atrial Fibrillation (AF) is one of the atrial cardiac arrythmias with highest prevalence in the elderly. In order to use the electrocardiogram (ECG) as a noninvasive tool for AF analysis, we need to separate the atrial activity (AA) from other cardioelectric signals. In this matter, Blind Source Separation (BSS) techniques are able to perform a multi-lead analysis of the ECG with the aim to obtain a set of independent sources where the AA is included. Two different assumptions on the mixing model in the human body can be done. Firstly, the instantaneous mixing model can be assumed in spite of the inaccuracy of this approximation. Secondly, the convolutive model is a more realistic model where weighted and delayed contributions in the generation of the electrocardiogram signals are considered. In this paper, a comparison between the performance of both models in the extraction of the AA in AF episodes is developed by analyzing the reults of five distinct BSS algorithms.

Keywords

Atrial Fibrillation Independent Component Analysis Blind Source Separation Atrial Activity Ventricular Activity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Carlos Vayá
    • 1
  • José Joaquín Rieta
    • 1
  • César Sánchez
    • 2
  • David Moratal
    • 1
  1. 1.Bioengineering, Electronics, Telemedicine and Medical Computer Science Research GroupValencia University of TechnologyGandía (Valencia)Spain
  2. 2.Innovation in BioengineeringCastilla-La Mancha UniversityCuencaSpain

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